Iet Generation Transmission & Distribution最新文献_第7页

IF 2.6 4区工程技术

Iet Generation Transmission & Distribution Pub Date : 2025-12-23

引用次数: 0

Battery-Aided Personalised Protection Strategy for Achieving Differential Privacy of Electricity Consumption Data 实现用电量数据差异化隐私的电池辅助个性化保护策略

IF 2.6 4区工程技术

Iet Generation Transmission & Distribution Pub Date : 2025-12-23 DOI: 10.1049/gtd2.70221

Renjie Luo, Zehao Song, Xuexian Liu, Zhiyi Li

{"title":"Battery-Aided Personalised Protection Strategy for Achieving Differential Privacy of Electricity Consumption Data","authors":"Renjie Luo, Zehao Song, Xuexian Liu, Zhiyi Li","doi":"10.1049/gtd2.70221","DOIUrl":"https://doi.org/10.1049/gtd2.70221","url":null,"abstract":"With the continuous energy transition and digital industrial upgrades, the collection and analysis of electricity consumption data have become the foundation for supporting the intelligent power system operations and load-side energy efficiency optimisation. However, this development raises significant concerns regarding user privacy and potential data leakage. To address the issue, this paper proposes a battery-aided personalised differential privacy framework that generates physically realisable noise through battery operations, thereby overcoming grid stability limitations associated with conventional virtual noise injection methods. The proposed method enables adaptive privacy preservation through bounded noise probability functions with configurable privacy parameters and incorporates time-of-use pricing via a mean-drift mechanism to enhance the economic viability of battery dispatch strategies. A multi-objective sand cat swarm optimisation algorithm is employed to determine the optimal configuration of battery parameters. Numerical experiments demonstrate that the proposed method effectively defends against load forecasting attacks and significantly reduces electricity costs for users, offering a viable solution for balancing privacy protection and economic benefits in smart grids.","PeriodicalId":13261,"journal":{"name":"Iet Generation Transmission & Distribution","volume":"20 1","pages":""},"PeriodicalIF":2.6,"publicationDate":"2025-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/gtd2.70221","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145824778","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Charging-Load Prediction for Electric Vehicle Stations Using Correlation Analysis and RIME-CNN-LSTM-Attention Model 基于相关分析和RIME-CNN-LSTM-Attention模型的电动汽车充电站充电负荷预测

IF 2.6 4区工程技术

Iet Generation Transmission & Distribution Pub Date : 2025-12-23 DOI: 10.1049/gtd2.70219

Mao Yang, Wencheng Li, Peng Sun, Xin Su, Tao Huang, Tianyu Zheng

{"title":"Charging-Load Prediction for Electric Vehicle Stations Using Correlation Analysis and RIME-CNN-LSTM-Attention Model","authors":"Mao Yang, Wencheng Li, Peng Sun, Xin Su, Tao Huang, Tianyu Zheng","doi":"10.1049/gtd2.70219","DOIUrl":"https://doi.org/10.1049/gtd2.70219","url":null,"abstract":"The rapid growth of electric vehicles (EVs) has made accurate forecasting of charging station loads essential for ensuring grid stability and supporting infrastructure planning. While previous studies have investigated this problem, correlation-based feature selection remains relatively underexplored, which may lead to redundant features and reduced prediction accuracy. To address this issue, this paper proposes a hybrid forecasting model, RIME-CNN-LSTM-Attention, which integrates correlation-driven feature selection with advanced deep learning. Pearson, Spearman, and Kendall's tau-b analyses are first applied to identify the most influential factors affecting charging demand. The RIME algorithm is then used to optimise the hyperparameters of the CNN-LSTM network, while the attention mechanism dynamically emphasises critical load fluctuation periods. Case studies utilising actual charging station data illustrate that the proposed model substantially surpasses benchmark methodologies, thereby improving the accuracy and resilience of electric vehicle charging load forecasting.","PeriodicalId":13261,"journal":{"name":"Iet Generation Transmission & Distribution","volume":"20 1","pages":""},"PeriodicalIF":2.6,"publicationDate":"2025-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/gtd2.70219","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145825044","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

IF 2.6 4区工程技术

Iet Generation Transmission & Distribution Pub Date : 2025-12-23

引用次数: 0

IF 2.6 4区工程技术

Iet Generation Transmission & Distribution Pub Date : 2025-12-23

引用次数: 0

A Novel Framework for Spatial-Temporal Impact of Electric Vehicles Charging on Hosting Risk in Urban Distribution System: A Case Study of Shanghai 电动汽车充电对城市配电系统承载风险时空影响的新框架——以上海市为例

IF 2.6 4区工程技术

Iet Generation Transmission & Distribution Pub Date : 2025-12-18 DOI: 10.1049/gtd2.70183

Ying Du, Junxiang Zhang, Yuntian Chen, Haoran Zhang, Haoran Ji, Chengshan Wang, Jinyue Yan

{"title":"A Novel Framework for Spatial-Temporal Impact of Electric Vehicles Charging on Hosting Risk in Urban Distribution System: A Case Study of Shanghai","authors":"Ying Du, Junxiang Zhang, Yuntian Chen, Haoran Zhang, Haoran Ji, Chengshan Wang, Jinyue Yan","doi":"10.1049/gtd2.70183","DOIUrl":"https://doi.org/10.1049/gtd2.70183","url":null,"abstract":"Electric vehicles (EVs) integration into urban distribution system is surging globally, leading to an increase in the demand for EV charging load, which brought significant hosting risk in urban power systems, such as transformer overload. This study aims to characterize and model the spatial-temporal impact of EV charging on hosting risk of urban power systems across various urban scales considering the recent development of fast charging stations. To achieve this, it proposes a novel framework for calculating the hosting risk. This provides sufficient guiding information for maintaining reliability in urban power systems. In detail, we systematically model the EV charging patterns by using high-resolution real-world EV trajectory and charging record data. K-means clustering is utilized to identify typical charging patterns in various urban scales. Then, the novel hosting risk indicators considering the fast charging station development and the EV growth based on urban EV charging pattern distribution are proposed, where random forest is used to model the critical parameters. Finally, the hosting risk indicators under different urban scale and different EV charging conditions are illustrated. The finding indicates that the hosting risk of urban distribution system is closely related to the EV charging patterns, showing significant regional differences. In Shanghai, the overall city charging pattern exhibits a double-peak structure with morning and evening peaks at 10:14 and 21:04, respectively. The maximum 24-h hosting risk indicators for different charging types vary significantly, with Type 2 charging patterns showing the highest risk. The spatial-temporal changes of which can provide significant information for future plans of EV charging stations and reliability maintenance schemes.","PeriodicalId":13261,"journal":{"name":"Iet Generation Transmission & Distribution","volume":"20 1","pages":""},"PeriodicalIF":2.6,"publicationDate":"2025-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/gtd2.70183","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145772670","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

IF 2.6 4区工程技术

Iet Generation Transmission & Distribution Pub Date : 2025-12-18

引用次数: 0

Identification of Single Line-Ground Faults in Active Distribution Networks Based on R-KAN 基于R-KAN的有源配电网单线接地故障识别

IF 2.6 4区工程技术

Iet Generation Transmission & Distribution Pub Date : 2025-12-17 DOI: 10.1049/gtd2.70213

Ming Wang, Jun Chen, Yixing Ding, Xianggen Yin

{"title":"Identification of Single Line-Ground Faults in Active Distribution Networks Based on R-KAN","authors":"Ming Wang, Jun Chen, Yixing Ding, Xianggen Yin","doi":"10.1049/gtd2.70213","DOIUrl":"https://doi.org/10.1049/gtd2.70213","url":null,"abstract":"In active distribution networks (ADNs), power electronic devices like inverters introduce noise interference into fault monitoring signals, compromising identification accuracy. This challenge is particularly pronounced in high-impedance grounding faults, where weak fault signatures degrade conventional detection performance. This study proposes an improved residual CNN architecture, ResNet-Kolmogorov-Arnold-network (R-KAN), for accurate single line-ground fault (SLGF) identification. The method leverages the rich fault features contained in transient zero-sequence current (ZSC) and zero-sequence voltage (ZSV) waveforms following SLGFs in DG-integrated systems. The model employs superimposed ZSC-ZSV images as inputs and replaces standard ReLU activation with KAN functions, reducing linear components and computational burden. A comprehensive dataset generated through PSCAD simulations trains the R-KAN alongside conventional neural networks. Comparative evaluations demonstrate R-KAN's superior classification performance across multiple metrics. Rigorous testing, including high-resistance fault scenarios, noise interference conditions, and missing data cases confirms the model's enhanced generalization capability. Field validation using actual recorded waveforms further verifies the model's practical effectiveness in real-world SLGF identification. The proposed approach addresses critical challenges in modern ADNs by combining advanced network architecture with optimized feature extraction from transient zero-sequence components.","PeriodicalId":13261,"journal":{"name":"Iet Generation Transmission & Distribution","volume":"20 1","pages":""},"PeriodicalIF":2.6,"publicationDate":"2025-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/gtd2.70213","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145779578","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

IF 2.6 4区工程技术

Iet Generation Transmission & Distribution Pub Date : 2025-12-17

引用次数: 0

IF 2.6 4区工程技术

Iet Generation Transmission & Distribution Pub Date : 2025-12-17

引用次数: 0